

<!DOCTYPE html>
<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
<head>
  <meta charset="utf-8">
  
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <meta name="Description" content="scikit-learn: machine learning in Python">

  
  <title>sklearn.impute.KNNImputer &mdash; scikit-learn 0.22 documentation</title>
  
  <link rel="canonical" href="http://scikit-learn.org/stable/modules/generated/sklearn.impute.KNNImputer.html" />

  
  <link rel="shortcut icon" href="../../_static/favicon.ico"/>
  

  <link rel="stylesheet" href="../../_static/css/vendor/bootstrap.min.css" type="text/css" />
  <link rel="stylesheet" href="../../_static/gallery.css" type="text/css" />
  <link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
<script id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
<script src="../../_static/jquery.js"></script> 
</head>
<body>
<nav id="navbar" class="sk-docs-navbar navbar navbar-expand-md navbar-light bg-light py-0">
  <div class="container-fluid sk-docs-container px-0">
      <a class="navbar-brand py-0" href="../../index.html">
        <img
          class="sk-brand-img"
          src="../../_static/scikit-learn-logo-small.png"
          alt="logo"/>
      </a>
    <button
      id="sk-navbar-toggler"
      class="navbar-toggler"
      type="button"
      data-toggle="collapse"
      data-target="#navbarSupportedContent"
      aria-controls="navbarSupportedContent"
      aria-expanded="false"
      aria-label="Toggle navigation"
    >
      <span class="navbar-toggler-icon"></span>
    </button>

    <div class="sk-navbar-collapse collapse navbar-collapse" id="navbarSupportedContent">
      <ul class="navbar-nav mr-auto">
        <li class="nav-item">
          <a class="sk-nav-link nav-link" href="../../install.html">Install</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link" href="../../user_guide.html">User Guide</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link" href="../classes.html">API</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link" href="../../auto_examples/index.html">Examples</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../getting_started.html">Getting Started</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../tutorial/index.html">Tutorial</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../glossary.html">Glossary</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../developers/index.html">Development</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../faq.html">FAQ</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../related_projects.html">Related packages</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../roadmap.html">Roadmap</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../about.html">About us</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="https://github.com/scikit-learn/scikit-learn">GitHub</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="https://scikit-learn.org/dev/versions.html">Other Versions</a>
        </li>
        <li class="nav-item dropdown nav-more-item-dropdown">
          <a class="sk-nav-link nav-link dropdown-toggle" href="#" id="navbarDropdown" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">More</a>
          <div class="dropdown-menu" aria-labelledby="navbarDropdown">
              <a class="sk-nav-dropdown-item dropdown-item" href="../../getting_started.html">Getting Started</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../tutorial/index.html">Tutorial</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../glossary.html">Glossary</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../developers/index.html">Development</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../faq.html">FAQ</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../related_projects.html">Related packages</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../roadmap.html">Roadmap</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../about.html">About us</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="https://github.com/scikit-learn/scikit-learn">GitHub</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="https://scikit-learn.org/dev/versions.html">Other Versions</a>
          </div>
        </li>
      </ul>
      <div id="searchbox" role="search">
          <div class="searchformwrapper">
          <form class="search" action="../../search.html" method="get">
            <input class="sk-search-text-input" type="text" name="q" aria-labelledby="searchlabel" />
            <input class="sk-search-text-btn" type="submit" value="Go" />
          </form>
          </div>
      </div>
    </div>
  </div>
</nav>
<div class="d-flex" id="sk-doc-wrapper">
    <input type="checkbox" name="sk-toggle-checkbox" id="sk-toggle-checkbox">
    <label id="sk-sidemenu-toggle" class="sk-btn-toggle-toc btn sk-btn-primary" for="sk-toggle-checkbox">Toggle Menu</label>
    <div id="sk-sidebar-wrapper" class="border-right">
      <div class="sk-sidebar-toc-wrapper">
        <div class="sk-sidebar-toc-logo">
          <a href="../../index.html">
            <img
              class="sk-brand-img"
              src="../../_static/scikit-learn-logo-small.png"
              alt="logo"/>
          </a>
        </div>
        <div class="btn-group w-100 mb-2" role="group" aria-label="rellinks">
            <a href="sklearn.impute.MissingIndicator.html" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="sklearn.impute.MissingIndicator">Prev</a><a href="../classes.html" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="API Reference">Up</a>
            <a href="sklearn.inspection.partial_dependence.html" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="sklearn.inspection.partial_dependence">Next</a>
        </div>
        <div class="alert alert-danger p-1 mb-2" role="alert">
          <p class="text-center mb-0">
          <strong>scikit-learn 0.22</strong><br/>
          <a href="http://scikit-learn.org/dev/versions.html">Other versions</a>
          </p>
        </div>
        <div class="alert alert-warning p-1 mb-2" role="alert">
          <p class="text-center mb-0">
            Please <a class="font-weight-bold" href="../../about.html#citing-scikit-learn"><string>cite us</string></a> if you use the software.
          </p>
        </div>
          <div class="sk-sidebar-toc">
            <ul>
<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.impute</span></code>.KNNImputer</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-impute-knnimputer">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.impute.KNNImputer</span></code></a></li>
</ul>
</li>
</ul>

          </div>
      </div>
    </div>
    <div id="sk-page-content-wrapper">
      <div class="sk-page-content container-fluid body px-md-3" role="main">
        
  <div class="section" id="sklearn-impute-knnimputer">
<h1><a class="reference internal" href="../classes.html#module-sklearn.impute" title="sklearn.impute"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.impute</span></code></a>.KNNImputer<a class="headerlink" href="#sklearn-impute-knnimputer" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.impute.KNNImputer">
<em class="property">class </em><code class="sig-prename descclassname">sklearn.impute.</code><code class="sig-name descname">KNNImputer</code><span class="sig-paren">(</span><em class="sig-param">missing_values=nan</em>, <em class="sig-param">n_neighbors=5</em>, <em class="sig-param">weights='uniform'</em>, <em class="sig-param">metric='nan_euclidean'</em>, <em class="sig-param">copy=True</em>, <em class="sig-param">add_indicator=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/impute/_knn.py#L19"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.impute.KNNImputer" title="Permalink to this definition">¶</a></dt>
<dd><p>Imputation for completing missing values using k-Nearest Neighbors.</p>
<p>Each sample’s missing values are imputed using the mean value from
<code class="docutils literal notranslate"><span class="pre">n_neighbors</span></code> nearest neighbors found in the training set. Two samples are
close if the features that neither is missing are close.</p>
<p>Read more in the <a class="reference internal" href="../impute.html#knnimpute"><span class="std std-ref">User Guide</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.22.</span></p>
</div>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>missing_values</strong><span class="classifier">number, string, np.nan or None, default=`np.nan`</span></dt><dd><p>The placeholder for the missing values. All occurrences of
<code class="docutils literal notranslate"><span class="pre">missing_values</span></code> will be imputed.</p>
</dd>
<dt><strong>n_neighbors</strong><span class="classifier">int, default=5</span></dt><dd><p>Number of neighboring samples to use for imputation.</p>
</dd>
<dt><strong>weights</strong><span class="classifier">{‘uniform’, ‘distance’} or callable, default=’uniform’</span></dt><dd><p>Weight function used in prediction.  Possible values:</p>
<ul class="simple">
<li><p>‘uniform’ : uniform weights. All points in each neighborhood are
weighted equally.</p></li>
<li><p>‘distance’ : weight points by the inverse of their distance.
in this case, closer neighbors of a query point will have a
greater influence than neighbors which are further away.</p></li>
<li><p>callable : a user-defined function which accepts an
array of distances, and returns an array of the same shape
containing the weights.</p></li>
</ul>
</dd>
<dt><strong>metric</strong><span class="classifier">{‘nan_euclidean’} or callable, default=’nan_euclidean’</span></dt><dd><p>Distance metric for searching neighbors. Possible values:</p>
<ul class="simple">
<li><p>‘nan_euclidean’</p></li>
<li><p>callable : a user-defined function which conforms to the definition
of <code class="docutils literal notranslate"><span class="pre">_pairwise_callable(X,</span> <span class="pre">Y,</span> <span class="pre">metric,</span> <span class="pre">**kwds)</span></code>. The function
accepts two arrays, X and Y, and a <code class="docutils literal notranslate"><span class="pre">missing_values</span></code> keyword in
<code class="docutils literal notranslate"><span class="pre">kwds</span></code> and returns a scalar distance value.</p></li>
</ul>
</dd>
<dt><strong>copy</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, a copy of X will be created. If False, imputation will
be done in-place whenever possible.</p>
</dd>
<dt><strong>add_indicator</strong><span class="classifier">bool, default=False</span></dt><dd><p>If True, a <a class="reference internal" href="sklearn.impute.MissingIndicator.html#sklearn.impute.MissingIndicator" title="sklearn.impute.MissingIndicator"><code class="xref py py-class docutils literal notranslate"><span class="pre">MissingIndicator</span></code></a> transform will stack onto the
output of the imputer’s transform. This allows a predictive estimator
to account for missingness despite imputation. If a feature has no
missing values at fit/train time, the feature won’t appear on the
missing indicator even if there are missing values at transform/test
time.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes</dt>
<dd class="field-even"><dl>
<dt><strong>indicator_</strong><span class="classifier"><a class="reference internal" href="sklearn.impute.MissingIndicator.html#sklearn.impute.MissingIndicator" title="sklearn.impute.MissingIndicator"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.impute.MissingIndicator</span></code></a></span></dt><dd><p>Indicator used to add binary indicators for missing values.
<code class="docutils literal notranslate"><span class="pre">None</span></code> if add_indicator is False.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">References</p>
<ul class="simple">
<li><p>Olga Troyanskaya, Michael Cantor, Gavin Sherlock, Pat Brown, Trevor
Hastie, Robert Tibshirani, David Botstein and Russ B. Altman, Missing
value estimation methods for DNA microarrays, BIOINFORMATICS Vol. 17
no. 6, 2001 Pages 520-525.</p></li>
</ul>
<p class="rubric">Examples</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.impute</span> <span class="kn">import</span> <span class="n">KNNImputer</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">7</span><span class="p">]]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">imputer</span> <span class="o">=</span> <span class="n">KNNImputer</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">imputer</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="go">array([[1. , 2. , 4. ],</span>
<span class="go">       [3. , 4. , 3. ],</span>
<span class="go">       [5.5, 6. , 5. ],</span>
<span class="go">       [8. , 8. , 7. ]])</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.impute.KNNImputer.fit" title="sklearn.impute.KNNImputer.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(self, X[, y])</p></td>
<td><p>Fit the imputer on X.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.impute.KNNImputer.fit_transform" title="sklearn.impute.KNNImputer.fit_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_transform</span></code></a>(self, X[, y])</p></td>
<td><p>Fit to data, then transform it.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.impute.KNNImputer.get_params" title="sklearn.impute.KNNImputer.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>(self[, deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.impute.KNNImputer.set_params" title="sklearn.impute.KNNImputer.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(self, \*\*params)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.impute.KNNImputer.transform" title="sklearn.impute.KNNImputer.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a>(self, X)</p></td>
<td><p>Impute all missing values in X.</p></td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.impute.KNNImputer.__init__">
<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">missing_values=nan</em>, <em class="sig-param">n_neighbors=5</em>, <em class="sig-param">weights='uniform'</em>, <em class="sig-param">metric='nan_euclidean'</em>, <em class="sig-param">copy=True</em>, <em class="sig-param">add_indicator=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/impute/_knn.py#L98"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.impute.KNNImputer.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.impute.KNNImputer.fit">
<code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/impute/_knn.py#L156"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.impute.KNNImputer.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit the imputer on X.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like shape of (n_samples, n_features)</span></dt><dd><p>Input data, where <code class="docutils literal notranslate"><span class="pre">n_samples</span></code> is the number of samples and
<code class="docutils literal notranslate"><span class="pre">n_features</span></code> is the number of features.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd></dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.impute.KNNImputer.fit_transform">
<code class="sig-name descname">fit_transform</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y=None</em>, <em class="sig-param">**fit_params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L544"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.impute.KNNImputer.fit_transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit to data, then transform it.</p>
<p>Fits transformer to X and y with optional parameters fit_params
and returns a transformed version of X.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">numpy array of shape [n_samples, n_features]</span></dt><dd><p>Training set.</p>
</dd>
<dt><strong>y</strong><span class="classifier">numpy array of shape [n_samples]</span></dt><dd><p>Target values.</p>
</dd>
<dt><strong>**fit_params</strong><span class="classifier">dict</span></dt><dd><p>Additional fit parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_new</strong><span class="classifier">numpy array of shape [n_samples, n_features_new]</span></dt><dd><p>Transformed array.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.impute.KNNImputer.get_params">
<code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L173"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.impute.KNNImputer.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">mapping of string to any</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.impute.KNNImputer.set_params">
<code class="sig-name descname">set_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L205"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.impute.KNNImputer.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code> so that it’s possible to update each
component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.impute.KNNImputer.transform">
<code class="sig-name descname">transform</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/impute/_knn.py#L190"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.impute.KNNImputer.transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Impute all missing values in X.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>The input data to complete.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_output_features)</span></dt><dd><p>The imputed dataset. <code class="docutils literal notranslate"><span class="pre">n_output_features</span></code> is the number of features
that is not always missing during <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

</dd></dl>

<div class="section" id="examples-using-sklearn-impute-knnimputer">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.impute.KNNImputer</span></code><a class="headerlink" href="#examples-using-sklearn-impute-knnimputer" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="Missing values can be replaced by the mean, the median or the most frequent value using the bas..."><div class="figure align-default" id="id1">
<img alt="../../_images/sphx_glr_plot_missing_values_thumb.png" src="../../_images/sphx_glr_plot_missing_values_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/impute/plot_missing_values.html#sphx-glr-auto-examples-impute-plot-missing-values-py"><span class="std std-ref">Imputing missing values before building an estimator</span></a></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="We are pleased to announce the release of scikit-learn 0.22, which comes with many bug fixes an..."><div class="figure align-default" id="id2">
<img alt="../../_images/sphx_glr_plot_release_highlights_0_22_0_thumb.png" src="../../_images/sphx_glr_plot_release_highlights_0_22_0_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/release_highlights/plot_release_highlights_0_22_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-0-22-0-py"><span class="std std-ref">Release Highlights for scikit-learn 0.22</span></a></span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
</div>
</div><div class="clearer"></div></div>
</div>


      </div>
    <div class="container">
      <footer class="sk-content-footer">
            &copy; 2007 - 2019, scikit-learn developers (BSD License).
          <a href="../../_sources/modules/generated/sklearn.impute.KNNImputer.rst.txt" rel="nofollow">Show this page source</a>
      </footer>
    </div>
  </div>
</div>
<script src="../../_static/js/vendor/bootstrap.min.js"></script>

<script>
    window.ga=window.ga||function(){(ga.q=ga.q||[]).push(arguments)};ga.l=+new Date;
    ga('create', 'UA-22606712-2', 'auto');
    ga('set', 'anonymizeIp', true);
    ga('send', 'pageview');
</script>
<script async src='https://www.google-analytics.com/analytics.js'></script>


<script>
$(document).ready(function() {
    /* Add a [>>>] button on the top-right corner of code samples to hide
     * the >>> and ... prompts and the output and thus make the code
     * copyable. */
    var div = $('.highlight-python .highlight,' +
                '.highlight-python3 .highlight,' +
                '.highlight-pycon .highlight,' +
		'.highlight-default .highlight')
    var pre = div.find('pre');

    // get the styles from the current theme
    pre.parent().parent().css('position', 'relative');
    var hide_text = 'Hide prompts and outputs';
    var show_text = 'Show prompts and outputs';

    // create and add the button to all the code blocks that contain >>>
    div.each(function(index) {
        var jthis = $(this);
        if (jthis.find('.gp').length > 0) {
            var button = $('<span class="copybutton">&gt;&gt;&gt;</span>');
            button.attr('title', hide_text);
            button.data('hidden', 'false');
            jthis.prepend(button);
        }
        // tracebacks (.gt) contain bare text elements that need to be
        // wrapped in a span to work with .nextUntil() (see later)
        jthis.find('pre:has(.gt)').contents().filter(function() {
            return ((this.nodeType == 3) && (this.data.trim().length > 0));
        }).wrap('<span>');
    });

    // define the behavior of the button when it's clicked
    $('.copybutton').click(function(e){
        e.preventDefault();
        var button = $(this);
        if (button.data('hidden') === 'false') {
            // hide the code output
            button.parent().find('.go, .gp, .gt').hide();
            button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'hidden');
            button.css('text-decoration', 'line-through');
            button.attr('title', show_text);
            button.data('hidden', 'true');
        } else {
            // show the code output
            button.parent().find('.go, .gp, .gt').show();
            button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'visible');
            button.css('text-decoration', 'none');
            button.attr('title', hide_text);
            button.data('hidden', 'false');
        }
    });

	/*** Add permalink buttons next to glossary terms ***/
	$('dl.glossary > dt[id]').append(function() {
		return ('<a class="headerlink" href="#' +
			    this.getAttribute('id') +
			    '" title="Permalink to this term">¶</a>');
	});
  /*** Hide navbar when scrolling down ***/
  // Returns true when headerlink target matches hash in url
  (function() {
    hashTargetOnTop = function() {
        var hash = window.location.hash;
        if ( hash.length < 2 ) { return false; }

        var target = document.getElementById( hash.slice(1) );
        if ( target === null ) { return false; }

        var top = target.getBoundingClientRect().top;
        return (top < 2) && (top > -2);
    };

    // Hide navbar on load if hash target is on top
    var navBar = document.getElementById("navbar");
    var navBarToggler = document.getElementById("sk-navbar-toggler");
    var navBarHeightHidden = "-" + navBar.getBoundingClientRect().height + "px";
    var $window = $(window);

    hideNavBar = function() {
        navBar.style.top = navBarHeightHidden;
    };

    showNavBar = function() {
        navBar.style.top = "0";
    }

    if (hashTargetOnTop()) {
        hideNavBar()
    }

    var prevScrollpos = window.pageYOffset;
    hideOnScroll = function(lastScrollTop) {
        if (($window.width() < 768) && (navBarToggler.getAttribute("aria-expanded") === 'true')) {
            return;
        }
        if (lastScrollTop > 2 && (prevScrollpos <= lastScrollTop) || hashTargetOnTop()){
            hideNavBar()
        } else {
            showNavBar()
        }
        prevScrollpos = lastScrollTop;
    };

    /*** high preformance scroll event listener***/
    var raf = window.requestAnimationFrame ||
        window.webkitRequestAnimationFrame ||
        window.mozRequestAnimationFrame ||
        window.msRequestAnimationFrame ||
        window.oRequestAnimationFrame;
    var lastScrollTop = $window.scrollTop();

    if (raf) {
        loop();
    }

    function loop() {
        var scrollTop = $window.scrollTop();
        if (lastScrollTop === scrollTop) {
            raf(loop);
            return;
        } else {
            lastScrollTop = scrollTop;
            hideOnScroll(lastScrollTop);
            raf(loop);
        }
    }
  })();
});

</script>
    
<script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml.js"></script>
    
</body>
</html>